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Remote Sensing Technology and Application  2006, Vol. 21 Issue (1): 18-24    DOI: 10.11873/j.issn.1004-0323.2006.1.18
article     
Staticical Analysis on Spectral and Textural Features of Clouds
ZHU Ya-ping 1, 2, LIU Jian-wen2, BAI Jie2
( 1. Institute of Meteorology , PLA University of Science and Technology , Nanjing 211101, China;
( 2. Institute of Aviation Meteorology , Beijing 100085, China)
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Abstract  

Data sets of 11 cloud/surface classes are collected from daytime geostationary satellite imagery
data over middle latitude and low latitude regions in summer . 656 samples are randomly selected, and then
116 spectral and textural features derived from these samples are statistically investigated, curves of all
features are drawn and analyzed. After feature selection, sensitivity tests of two classifiers-stepwise
cluster and fuzzy cluster are explored by using of more significant feature arrays. For spectral features,
results of feature curves analysis indicate that spectral characteristics are more significant than those of
textural characteristics, the spectral features of IR or WV except Std show more distinguishingly than
those of VIS, especially for low - or mid-level and cirrus; both results of sensitivity tests from two
classifiers suggest that spectral features provide the major information of cloud classification, which are
main indexes of cloud pattern discrimination. Results of textur al feature curves indicate first order
probability vectors ( FOPV ) demonstrate more obviously than those of gray level difference vectors
( GLDV) , especially CON of four channels and HOM of WV; results of tests show that textural features
play an important role in cloud type identification, both classifiers give higher accuracies after adding
textural features, combing FODV and spectral features significantly improve classification results achieved
using only spectral features, but the accuracy of adding GLDV based on FODV and spectral features has
little distinction with those of adding FOPV based on spectral features, which indicates that textural
characteristics of FODV have more encouraging ability to distinguish between cloud/ surfaces than those of
GLDV, consisting with results of feature curves analysis.

Key words:   Cloud classification      Spectral features      Textural features     
Received:  03 June 2005      Published:  27 September 2011
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Cite this article: 

ZHU Ya-ping , LIU Jian-wen, BAI Jie. Staticical Analysis on Spectral and Textural Features of Clouds. Remote Sensing Technology and Application, 2006, 21(1): 18-24.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2006.1.18     OR     http://www.rsta.ac.cn/EN/Y2006/V21/I1/18

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